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What to check about a Python machine learning book before buying it


With so many books on Python machine learning, making a choice is becoming increasingly difficult. You’re investing both your time and money to learn something that can open new career paths for you. It would a disappointment to get halfway through a 700-page machine learning book to realize it’s not for you.

Having read and reviewed many books on Python machine learning, I can attest that every volume is unique in its own right. And with so many books having similar titles, the choice can be confusing, especially if you’re at the beginning of your machine learning journey.

Here are three things that will help you better evaluate a Python machine learning book before buying it.

The prerequisites

Every book on programming has a section in the preface or intro where the authors describe the book’s intended audience. This part is usually titled “prerequisites” or “who should read this book.” If you’re at a bookstore, pick up the book and flip to this section. If you’re buying online, Amazon and other online stores let you view the first few pages of the book, including the prerequisites section.

Here are a few things that are worth looking out for in the prerequisites section:

  • Python skills: Some books use simple code snippets just to prove a concept, while others make use of advanced Python features such as list comprehensions, slicing, with statements, parameter unpacking, and more. While it’s not fair to expect the book to create a comprehensive list of all techniques it will use, it should at least mention the level of skills you need. (Most machine learning books presume you have a basic understanding of data science and Python.)
  • Python libraries: Python machine learning books usually use ScikitLearn (and sometimes SciPy) to implement algorithms. Books on deep learning cover TensorFlow, Keras, and PyTorch. But these books also make use of scientific libraries such as Numpy, Pandas, and Matplotlib to load and manipulate data. Some books will cover these libraries while others will tell you that you should already have experience with them. (To be clear, these libraries have dedicated books that span several hundred pages.)
  • Python tools: Most Python machine learning developers prefer the Jupyter Notebook, a web-based interface that lets you code and test your algorithms in one place and save the results in HTML format. If a book will be using Jupyter, it should state whether it will take you through the installation and setup.
  • Math skills: Under the hood, machine learning involves a great deal of linear algebra, calculus, and statistics. Some books will try to describe the mechanics of machine learning algorithms through conceptual descriptions and drawings. Others will just spill the mathematical equations and let you figure it out for yourself (and some books will try to bridge the gap between the two with step-by-step description of the algorithms). Most books tell you how much math skills you need to read the book.